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It is not rocket science. You can become a data analyst in 2022 if you put your mind to it.
If you want to pursue a career in data science, consider the following five steps:
- Obtain a bachelor’s degree in an area that emphasises statistical and analytical abilities, such as math or computer science.
- Learn essential data analytics skills for free.
- Think about certification.
- Get your first job as an entry-level data analyst.
- Study for a master’s degree in data analytics.
Who is a data analyst, and what do they do?
A data analyst gathers, analyses, and runs statistical analysis on massive datasets. They learn how to use data to answer questions and solve issues. Data analysis has progressed in tandem with the advancement of computers and the increasing convergence of technology. The introduction of the relational database breathed new life into data analysts, allowing them to extract data from databases using SQL (pronounced “sequel” or “s-q-l”).
Data Analyst job description.
Most data analytics professions entail acquiring and cleansing data to find patterns and business insights. The day-to-day data analyst work varies based on the sector, firm, or kind of data analytics you consider your speciality. Data analysts may establish and manage relational databases and systems for several departments within their firm, utilising business intelligence tools, Tableau, and scripting.
Most data analysts collaborate with IT teams, management, and/or data scientists to identify organisational goals. They collect, clean, and analyse data from primary and secondary sources before analysing and interpreting the results with conventional statistical tools and methodologies. In most situations, they detect trends, correlations, and patterns in large amounts of data and uncover new chances for process improvement. Data analysts must also report their results and inform key stakeholders about the next actions.
Data analysts’ skills and qualifications.
- Programming Languages (R/SAS): Data analysts should be fluent in at least one programming language and have a working understanding of a few others. Data analysts collect, clean, analyse, and visualise data using computer languages such as R and SAS.
- Analytical and Creative Thinking: Curiosity and inventiveness are important characteristics of a competent data analyst. Having a solid understanding of statistical procedures is necessary, but it’s even more crucial to approach challenges creatively and analytically. This will assist the analyst in developing fascinating research questions that will improve a company’s comprehension of the subject.
- Strong and Effective Communication: Data analysts must properly communicate their results to a group of readers or a small team of executives making business choices. The key to success is effective communication.
- Effective data visualisation necessitates trial and error. A skilled data analyst knows which graphs to employ, how to scale visualisations, and which charts to utilise based on the audience.
- Data Warehousing: Some data analysts labour behind the scenes. They establish a data warehouse by connecting databases from many sources and using querying languages to locate and manage data.
- SQL databases: SQL databases are relational databases that contain structured data. Data is kept in tables, and a data analyst extracts information from several tables to analyse.
- Database Querying Languages: SQL is the most often used querying language among data analysts, and there are other versions of this language, including PostreSQL, T-SQL, and PL/SQL (Procedural Language/SQL).
- Data Mining, Cleaning, and Munging: When data cannot be neatly stored in a database, data analysts must acquire unstructured data using different technologies. Once they obtain sufficient data, they clean and process it using programming.
- Sophisticated Microsoft Excel: Data analysts should be comfortable with Excel and be familiar with advanced modelling and analytics approaches.
- Machine Learning: Data analysts with machine learning abilities are extremely useful, although machine learning is not a required ability in most data analyst professions.
What tools do Data analysts use?
Here are some other tools that data analysts utilise on the job:
- Google Analytics (GA): GA assists analysts in understanding customer data, such as patterns and areas of customer experience that need to be improved on landing pages or calls to action (CTAs).
- Tableau: Tableau is used by analysts to gather and analyse data. They may design and share dashboards with other team members and generate visualisations.
- Jupyter Notebook software: Data analysts can easily test programs using Jupyter notebooks. Because of the markdown capability, non-technical people appreciate the straightforward look of jupyter notebooks.
- Github: Github is a website for sharing and constructing technological projects. A must-have for data analysts who work with object-oriented programming.
- AWS S3: AWS S3 is a type of cloud storage service. Data analysts may use it to store and retrieve big datasets.
Overview.
Market research analyst positions are expected to grow by 22%, and management analyst positions are expected to grow by 14%. Because data analysts may work in a wide range of industries – including banking, healthcare, information, manufacturing, professional services, and retail – the advancement of technology has increased the number of analysts. We are always acquiring data; its structure and the use of predictive analysis aid society in becoming a better version of itself.
Today’s data analysts must be ready for change. Analyst positions are growing increasingly sophisticated. Experienced analysts use modelling and predictive analytics approaches to create meaningful insights and actions. Then you have to explain your findings to a room full of puzzled laymen. In other words, you must evolve from data analysts to data scientists and effective communicators.
On a scale of 1-10, how enthusiastic are you to become the next hottest data analyst in the tech space? Perhaps something else is for you.
